Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures
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David P. Kreil | Steven P. Lund | Paweł P. Łabaj | Sarah A. Munro | S. Hochreiter | Simon M Lin | Djork-Arné Clevert | C. Mason | M. Salit | S. Lund | Leming Shi | R. Setterquist | W. Tong | Charles Wang | H. Hong | P. S. Pine | Z. Su | Sheng Li | W. Shi | Joshua Xu | A. Conesa | N. Jafari | Yang Liao | Javier Santoyo-Lopez | G. Smyth | Jian Wang | H. Binder | J. Dopazo | M. Fasold | J. Meehan | N. Stralis-Pavese | Yong Yang | Z. Ye | Ying Yu | S. Munro | Zhan Ye | Wei Shi | P. Pine | Sepp Hochreiter
[1] Martin Vingron,et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.
[2] S. Dudoit,et al. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .
[3] David Botstein,et al. BMC Genomics BioMed Central Methodology article Universal Reference RNA as a standard for microarray experiments , 2004 .
[4] P. Kemmeren,et al. Monitoring global messenger RNA changes in externally controlled microarray experiments , 2003, EMBO reports.
[5] Jean YH Yang,et al. Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.
[6] Thomas Lengauer,et al. ROCR: visualizing classifier performance in R , 2005, Bioinform..
[7] Kathleen F. Kerr,et al. The External RNA Controls Consortium: a progress report , 2005, Nature Methods.
[8] Rafael A. Irizarry,et al. Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005 .
[9] Weida Tong,et al. Evaluation of external RNA controls for the assessment of microarray performance , 2006, Nature Biotechnology.
[10] Klaus Obermayer,et al. A new summarization method for affymetrix probe level data , 2006, Bioinform..
[11] Maqc Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements , 2006, Nature Biotechnology.
[12] Leming Shi,et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques , 2006, Nature Biotechnology.
[13] Marc Salit,et al. Standards in gene expression microarray experiments. , 2006, Methods in enzymology.
[14] P S Pine,et al. Use of diagnostic accuracy as a metric for evaluating laboratory proficiency with microarray assays using mixed-tissue RNA reference samples. , 2008, Pharmacogenomics.
[15] R. Irizarry,et al. Consolidated strategy for the analysis of microarray spike-in data , 2008, Nucleic acids research.
[16] Hadley Wickham,et al. ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .
[17] Lior Pachter,et al. Sequence Analysis , 2020, Definitions.
[18] Richard Durbin,et al. Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .
[19] Ivo L. Hofacker,et al. Hybridization thermodynamics of NimbleGen Microarrays , 2010, BMC Bioinformatics.
[20] Jörg Rahnenführer,et al. Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit (2005): Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2009 .
[21] Steven J. M. Jones,et al. Alternative expression analysis by RNA sequencing , 2010, Nature Methods.
[22] M. Salit,et al. Exploring the use of internal and externalcontrols for assessing microarray technical performance , 2010, BMC Research Notes.
[23] Peter F. Stadler,et al. G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration , 2010, BMC Bioinformatics.
[24] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[25] M. Salit,et al. Synthetic Spike-in Standards for Rna-seq Experiments Material Supplemental Open Access License Commons Creative , 2022 .
[26] R. Sandberg,et al. Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.
[27] Davis J. McCarthy,et al. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.
[28] David A. Orlando,et al. Revisiting Global Gene Expression Analysis , 2012, Cell.
[29] Steven P Lund,et al. Statistical Applications in Genetics and Molecular Biology Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates , 2012 .
[30] Richard M Myers,et al. Transposase mediated construction of RNA-seq libraries. , 2012, Genome research.
[31] Chris Williams,et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization , 2012, Bioinform..
[32] Peter A. Flach,et al. Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them) , 2012, Briefings Bioinform..
[33] W. Shi,et al. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote , 2013, Nucleic acids research.
[34] Aviv Regev,et al. Corrigendum: Comparative analysis of RNA sequencing methods for degraded or low-input samples , 2013, Nature Methods.
[35] David P. Kreil,et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium , 2014, Nature Biotechnology.
[36] Sheng Li,et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study , 2014, Nature Biotechnology.
[37] David P. Kreil,et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance , 2014, Nature Biotechnology.
[38] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[39] Wei Shi,et al. Detecting and correcting systematic variation in large-scale RNA sequencing data , 2014, Nature Biotechnology.
[40] Wei Shi,et al. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..